Significant interest exists in systems integrated into or onto a mobile platform (e.g., a vehicle) that can detect objects and utilize the object-detection information to properly classify and identify the detected objects. Prior art systems are configured to detect such potential objects; however, said systems are not able to classify the detected objects without returning a large number of false positives, leading to inefficiencies and an increased computational burden. False positives occur when a system improperly classifies a detected object as belonging to a particular category of objects the system is intended to identify.
One way these systems have attempted to lower the false positive rate is by utilizing filters. Some examples of filters include long wave infrared (LWIR) filtering and filters based on classifiers, e.g., AdaBoost or Support Vector Machine (SVM) classifiers with scale and/or rotation invariant feature descriptors (like histogram of oriented gradients (HOG) or Scale Invariant Feature Transform).
Each type of filtering has its advantages and disadvantages. LWIR filtering may be easy to compute, however it has a high rate of false positives. Conventional classifiers that use HOG feature descriptors have a lower rate of false positives compared to LWIR filtering; however, it requires significant computational resources and can produce false negatives (i.e., a false negative occurs when a filter removes a candidate region, identified by a detector, that contains an object) when objects appear against complex and highly textured backgrounds because image gradient-based features become fragile in the presence of multiple gradient directions in a local image patch.
Furthermore, in general object detection and filtering, it is often required to search for an optimal region of interest (ROI) size and position to obtain valid classification scores. This is due to the sensitivity of the classifiers to ROI alignment as rigid placement of the local feature sampling windows inside the ROI become susceptible to different object configuration changes, such as different body poses in the case of pedestrian detection. This results in the need for an exhaustive search over multiple positions and scales for each input ROI.
Therefore, there is need in the art for a method and system that provides for efficient object detection and classification with a low false positive rate.